Python NumPy - 实现阈值上限的更快方法
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NumPy - Faster way to implement threshold value ceiling
提问by Jason
I'm writing a script to modify the luminance of a RGB image using NumPy and CV2 via converting from RGB to YCrCb and back again. However, the loop I'm using takes a while to execute, and am wondering if there is a faster way.
我正在编写一个脚本，通过从 RGB 转换为 YCrCb 并再次转换回来，使用 NumPy 和 CV2 修改 RGB 图像的亮度。但是，我使用的循环需要一段时间才能执行，我想知道是否有更快的方法。
import cv2 as cv, numpy as np
threshold = 64
image = cv.imread("motorist.jpg", -1)
image.shape # Evaluates to (1000, 1500, 3)
im = cv.cvtColor(image, cv.COLOR_RGB2YCR_CB)
for row in image:
for col in row:
if col[0] > threshold:
col[0] = threshold
image = cv.cvtColor(im, cv.COLOR_YCR_CB2RGB)
cv.imwrite("motorist_filtered.jpg", image)
That nested loop implementing the threshold comparison takes at least 5-7 seconds to execute. Is there a faster method to implement this functionality?
执行阈值比较的嵌套循环至少需要 5-7 秒才能执行。有没有更快的方法来实现这个功能？
采纳答案by Hooked
The idea is to create a maskthat lets you use the numpy's vectorization. Since the shape is (n,m,3), loop over the first two dimensions and grab the first index of the last dimension with [:,:,0]
这个想法是创建一个掩码，让您可以使用 numpy 的矢量化。由于形状是(n,m,3)，循环前两个维度并使用最后一个维度的第一个索引[:,:,0]
idx = image[:,:,0] > threshold
image[idx,0] = threshold
回答by YXD
You can use clip:
您可以使用clip：
Usage:
用法：
result = im.copy()
result[..., 0] = np.clip(im[..., 0], 0, threshold)
Or to modify in-place:
或者就地修改：
np.clip(im[..., 0], 0, threshold, out=im[..., 0])
回答by Martin
import numpy as np
image[..., 0] = np.minimum(image[..., 0], threshold)
Edit: Sorry, I can't add comments yet. I was feeling lazy yesterday. True about the in place modification but it is rather obvious or you don't need it. And laziness was a response to a lazy question - there is a function for 'everything' in numpy - just check the docs.
编辑：抱歉，我还不能添加评论。我昨天感觉很懒。关于就地修改是正确的，但它很明显，或者你不需要它。懒惰是对一个懒惰问题的回应——numpy 中有一个“一切”的功能——只需查看文档即可。
